Single experiment example
Leiv Rønneberg
05/09/2022
In the R package, we’ve attached two example datasets from a large
drug combination screening experiment on diffuse large B-cell lymphoma.
We’ll use these to show some simple use cases of the main functions and
how to interpret the results.
Let’s load in the first example and have a look at it
library(bayesynergy)
data("mathews_DLBCL")
y = mathews_DLBCL[[1]][[1]]
x = mathews_DLBCL[[1]][[2]]
head(cbind(y,x))
## Viability ibrutinib ispinesib
## [1,] 1.2295618 0.0000 0
## [2,] 1.0376006 0.1954 0
## [3,] 1.1813851 0.7812 0
## [4,] 0.5882688 3.1250 0
## [5,] 0.4666700 12.5000 0
## [6,] 0.2869514 50.0000 0
We see that the the measured viability scores are stored in the
vector y, while x is a matrix with two columns
giving the corresponding concentrations where the viability scores were
read off.
Fitting the regression model is simple enough, and can be done on
default settings simply by running the following code (where we add the
names of the drugs involved, the concentration units for plotting
purposes, and calculate the bayes factor).
fit = bayesynergy(y,x, drug_names = c("ibrutinib", "ispinesib"),
units = c("nM","nM"),bayes_factor = T)
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## Calculating the Bayes factor
The resulting model can be summarised by running
## mean se_mean sd 2.5% 50% 97.5% n_eff Rhat
## la_1[1] 0.3324 0.001962 0.0756 1.52e-01 3.43e-01 0.454 1485 1.001
## la_2[1] 0.3855 0.003833 0.0648 1.33e-01 3.96e-01 0.454 286 1.018
## log10_ec50_1 0.4802 0.003984 0.1572 2.48e-01 4.46e-01 0.868 1558 1.001
## log10_ec50_2 -1.0666 0.021021 1.0250 -3.32e+00 -9.35e-01 0.458 2378 1.002
## slope_1 2.0245 0.018697 0.9427 8.45e-01 1.79e+00 4.438 2542 1.000
## slope_2 1.4674 0.021346 1.1340 9.17e-02 1.19e+00 4.546 2823 1.003
## ell 3.1224 0.033301 1.5600 1.22e+00 2.77e+00 7.234 2194 1.001
## sigma_f 0.8560 0.014862 0.8605 1.67e-01 6.09e-01 3.000 3352 0.999
## s 0.0968 0.000253 0.0148 7.34e-02 9.49e-02 0.132 3424 1.000
## dss_1 33.5777 0.045681 2.9939 2.76e+01 3.36e+01 39.597 4295 1.000
## dss_2 59.5034 0.042056 2.7268 5.38e+01 5.96e+01 64.800 4204 1.000
## rVUS_f 82.7529 0.014156 0.8803 8.10e+01 8.28e+01 84.431 3867 1.000
## rVUS_p0 73.1228 0.033638 2.1766 6.87e+01 7.32e+01 77.369 4187 1.000
## VUS_Delta -9.6301 0.035468 2.3363 -1.44e+01 -9.58e+00 -5.102 4339 1.000
## VUS_syn -9.6737 0.034864 2.2953 -1.44e+01 -9.61e+00 -5.346 4334 1.000
## VUS_ant 0.0436 0.001794 0.1079 5.09e-06 8.17e-05 0.340 3620 1.000
##
## log-Pseudo Marginal Likelihood (LPML) = 52.11036
## Estimated Bayes factor in favor of full model over non-interaction only model: 29.01898
which gives posterior summaries of the parameters of the model.
In addition, the model calculates summary statistics of the
monotherapy curves and the dose-response surface including drug
sensitivity scores (DSS) for the two drugs in question, as well as the
volumes that capture the notion of efficacy (rVUS_f),
interaction (VUS_Delta), synergy (VUS_syn) and
interaction (VUS_ant).
As indicated, the total combined drug efficacy is around 80%
(rVUS_f), of which around 70 percentage points can be
attributed to \(p_0\)
(rVUS_p0), leaving room for 10 percentage points worth of
synergy (VUS_syn). We can also note that the model is
fairly certain of this effect, with a 95% credible interval given as
(-14.393, -5.346). The certainty of this is also verified by the Bayes
factor, which at 29.02 indicates strong evidence of an interaction
effect present in the model.
Visualization
3D interactive plots
The package can also generate 3D interactive plots by setting
plot3D = T. These are displayed as following using the
plotly library (Plotly Technologies Inc.
(2015)).
References
Plotly Technologies Inc. 2015.
“Collaborative Data
Science.” Montreal, QC: Plotly Technologies Inc. 2015.
https://plot.ly.
